The present invention relates generally to asset health monitoring and, more particularly, to a system and method for asset health monitoring using a multi-dimensional risk assessment.
A power distribution system/network or electrical grid/network ordinarily requires many components or assets to supply and transmit electrical power to loads that are connected to the power system. A power system may include, for example, generators, power stations, transmission systems, and distribution systems. Generators and power stations supply electrical power to transmission systems, which then transmit the electrical power to distribution systems. Distribution systems deliver the electrical power to loads such as, for example, residential, commercial, and industrial buildings. The necessary components or equipment to operate the transmission and distribution systems may include, for example, transformers, circuit breakers, relays, reclosers, capacitor banks, buses, and transmission lines. Those components can be quite expensive to replace, especially in a large power system with thousands of those components. To keep track of the condition of those components, many power systems implement asset health monitoring.
Asset health monitoring includes analyzing data about power system components in order to assess the risk of failure. Once the risk of failure has been determined, decisions can be made about when to perform maintenance on or replace the power system components and how to reconfigure the power flow in the system in order to perform the maintenance on or replace the components. In other words, if the asset health monitoring reveals that a power system component needs to be repaired or replaced, a course of action can be planned ahead of a system fault. However, maintenance personnel are usually limited in number and need to service a large number of assets over a fixed amount of time. Thus, it is crucial to manage the time spent by the maintenance personnel as efficiently as possible. If maintenance of a power system is not managed properly or disregarded entirely, the power system will eventually fail.
Various asset health monitoring techniques are used to determine when to perform maintenance on a power system component. A depth first approach may be used for network model maintenance. Predictive modeling techniques such as, for example, clustering, classification, association analysis, pattern discovery, regression, and anomaly detection may also be used. Mean absolute percentage error for pattern recognition may be implemented to forecast the load on the power system.
Depending on the technique used, the technique may leverage data from several different sources. The data used to manage power system maintenance may include information from an advanced metering infrastructure that may have a variety of meters in the system; a phasor measurement unit used to measure the electrical waves of the electrical grid; intelligent electronic devices that monitor, control, automate, and/or protect monitored equipment within the power system; or individual component sensors, for example. Offline data such as, for example, historical sensor data, field test and service data, or network model data may also be used.
However, the above-referenced asset health monitoring techniques suffer from deficiencies. In general, the asset health monitoring techniques do not take advantage of all the information relevant to assessing how much risk a deteriorating component poses to a power system. For example, while an asset health monitoring technique may consider data concerning equipment being monitored, it may not take into account data concerning other power system equipment that may be relevant to the future operation and of the monitored equipment. As an additional example, some asset health monitoring techniques use only historical fault and maintenance data to predict when a component will fail without incorporating any current information relevant to the condition of the component.
Furthermore, asset health monitoring techniques typically do not consider all of the factors influenced by the information collected. For example, asset health monitoring techniques often ignore the impact of a component failure or taking the component offline for maintenance. Moreover, asset health monitoring techniques fail to take into account the availability and accuracy of diagnostic information for power system equipment.
It would therefore be desirable to provide a system and method for asset health monitoring that assesses the risk of failure of power system component using all relevant data in order to optimize the efficiency of maintenance personnel.